2023
DOI: 10.3390/en16062878
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A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism

Abstract: Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit… Show more

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Cited by 8 publications
(3 citation statements)
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“…The bidirectional gated recurrent unit (BiGRU) adds a reverse layer to the forward layer of gated recurrent unit (GRU), and Figure 1 illustrates the structure of the BiGRU (Gong and Li, 2023).…”
Section: Bidirectional Gated Recurrent Unitmentioning
confidence: 99%
“…The bidirectional gated recurrent unit (BiGRU) adds a reverse layer to the forward layer of gated recurrent unit (GRU), and Figure 1 illustrates the structure of the BiGRU (Gong and Li, 2023).…”
Section: Bidirectional Gated Recurrent Unitmentioning
confidence: 99%
“…Electric load forecasting is an important aspect of modern power system management and a key research focus of power companies [1]. It comprises long-term, medium-term, and short-term forecasting, depending on the specific goals [2]. Notably, short-term load forecasting plays an important role in power generation planning and enables relevant departments to establish appropriate power dispatching plans [3,4], which is crucial for maintaining the safe and stable operation of the power system and enhancing its social benefits [5].…”
Section: Introductionmentioning
confidence: 99%
“…Genetic Algorithm (GA) is an optimization algorithm that imitates the process of natural selection. It uses a group of individuals to explore the optimal solution by applying genetic operators, such as crossover and mutation, to produce new solutions [11][12][13][14]. The optimization algorithm known as Ant Colony Optimization (ACO) draws inspiration from the foraging behavior of ants.…”
Section: Introductionmentioning
confidence: 99%